Search Results (3,648)

This paper presents a simulation-based methodology for automated tuning of a triple-loop controller (steering, throttle, and braking) for a Dallara single-seater race car. The approach targets on-track driving at handling limits, where strong nonlinearities and coupled dynamics dominate, treating the vehicle as a black box. Five controller gains are optimized via derivative-free pattern search, using reference trajectories from a professional driver in a Driver-in-the-Loop (DiL) simulator. Human-likeness is promoted by penalty terms on state and control trajectories while maximizing distance over a fixed horizon as a proxy for lap-time reduction. The application uses a high-fidelity multibody vehicle model with realistic tire, suspension, and actuator dynamics in the DiL environment, rather than simplified single-track representations. Contributions are: (i) effective application of derivative-free optimization to complex, high-dimensional, black-box vehicle systems; and (ii) a systematic, reproducible procedure for automatic tuning of controller parameters with a predetermined architecture to reproduce a professional driver’s performance and embed human-likeness. Optimization required approximately 2.4 h. Results show that the optimized controller improves track coverage by 63.6 m (1.1% increase) compared to manual tuning while maintaining a realistic driving style, offering a more systematic and reliable solution than manual, trial-and-error calibration. Full article

Electric unmanned vehicles applied in complex terrains such as agricultural, forestry, and deep-space exploration scenarios are often required to travel on uneven roads. In particular, during climbing processes, their driving stability and terrain adaptability are of critical importance. To address the above challenges, an electric unmanned vehicle with variable-diameter wheels is proposed. By adjusting the wheel diameter, the vehicle can modify its pitch and roll angles to adapt to uneven terrains. The core research focuses on the relationship between quasi-static stability and wheel diameter variation. First, the configuration and working principle of the electric unmanned vehicle with variable-diameter wheels are introduced, with particular emphasis on the mechanism principle of the novel variable-diameter wheel. A kinematic model between the electric cylinder input and wheel diameter in the variable-diameter wheel is established. On this basis, based on the FASM (Force-Angle Stability Margin)—a stable cone theory, the relationships between stability and wheel diameter variation were investigated separately under lateral, longitudinal, and 45° steering composite conditions on a slope. The results indicate that the unmanned vehicle can achieve omnidirectional attitude adjustment. Finally, the relationship between the electric cylinder input and stability is derived, which can provide a theoretical basis for the quasi-static stability control of outdoor electric unmanned vehicles. Full article

In response to the urgent requirement for sustainable power supply for deep-sea or offshore underwater sensing equipment, this work investigates autonomous power generation aboard marine vessels. The vertical vibrations induced by wave excitation at the bottom of the vessel are utilized to drive the vibration energy harvesters on the deck for power generation. In a scenario involving automatic steering, a multiplicity of magnetoelectric harvesters mounted on the deck would move vertically in response to surface wave motion, enabling continuous conversion of wave energy into electrical power. The key feature of this study is that the ship-based self-power generation system is simple to install and safe, with the vibration energy harvesters mounted above the sea surface to avoid the unpredictable underwater sea conditions. This study presents a numerical case analysis of a three-magnet energy harvester designed to generate induced electrical power under wave conditions characterized by a speed of V = 3.0 m/s, amplitude of Z_o = 0.4 m, and wavelength of λ = 2.0 m. Prior to optimizing the ship-based energy harvester, the mathematical model of a three-magnet vibration system was validated against experimental data to ensure accuracy. Subsequently, a sensitivity study was performed to evaluate the influence of wave parameters (e.g., amplitude and wavelength) and the harvester’s geometric parameters on the electrical power output. To maximize power generation, the flower pollination algorithm—an efficient bio-inspired optimization method known for its robustness in global search—was integrated with the objective function defined as the root-mean-square electrical power. Simulation results indicate that the optimized harvester is capable of producing up to 0.1943 W. These findings highlight the potential of ship-based energy harvesters as a sustainable and reliable source of electrical power. Full article

This paper presents ANAID (Autonomous Naturalistic obstacle-Avoidance Interaction Dataset), a new multimodal dataset designed to support research on autonomous driving, particularly with regard to obstacle avoidance and naturalistic driver–vehicle interaction. Data were collected using a Hyundai Tucson Hybrid equipped with a Comma-3X autonomous-driving development kit, combining high-resolution front-facing video with detailed CAN-bus telemetry. The dataset comprises four data collection campaigns, each corresponding to a single continuous driving session, yielding a total of 208 videos and 240,014 synchronized frames. In addition to the video data, the dataset provides vehicle state measurements (speed, acceleration, steering, pedal positions, turn signals, etc.) and an additional annotation layer identifying evasive maneuvers derived from steering-related signals. Data were recorded across four driving campaigns on an urban circuit at Universidad Europea de Madrid, capturing diverse real-world scenarios such as roundabouts, intersections, pedestrian areas, and segments requiring obstacle avoidance. A multi-stage processing pipeline aligns telemetry and visual data, extracts frames at 20 FPS, and detects evasive maneuvers using threshold-based time-series analysis. ANAID provides a fully aligned and non-destructive representation of naturalistic driving behavior, enabling research on control prediction, driver modeling, anomaly detection, and human–autonomy interaction in realistic traffic conditions. Full article

The rotary steerable system (RSS) is the core equipment for precise wellbore trajectory control in deep oil and gas drilling, and its performance is directly determined by the coordination and adaptability of the tool’s offset actuator and control platform. To overcome the limitations of complex control architectures and low positioning accuracy of conventional offset actuators for rotary steering drilling tools, a novel three hydraulic cylinder synchronous steering offset actuator driven by a drilling fluid rotary valve distributor, along with its dedicated control strategy, is proposed. Laboratory experiments and numerical simulations are performed to analyze the piston displacement characteristics of the three hydraulic cylinder under different drilling fluid flow rates and rotary valve rotational speeds. The results demonstrate that the proposed actuator exhibits controllable piston displacement behavior. The simulated and experimental data show consistent variation tendencies with a relative error of less than 8%, thus validating the reliability of the proposed numerical model. Increasing the flow rate from 1 to 1.5 L/s increases the cycle-averaged peak-to-peak piston displacement by 14.5 mm, while raising the rotational speed from 60 rpm to 120 rpm reduces it by 25.3 mm, corresponding to a dogleg severity variation of approximately 1.9–3.1°/30 m. Piston displacement deviations are mainly attributed to valve port machining tolerance, drilling fluid compressibility, pipeline pressure loss, and internal leakage, and these discrepancies are exacerbated as the rotary valve speed or flow rate increases. Finally, optimization strategies for improving synchronization performance are proposed, thereby providing theoretical and technical support for the engineering implementation and parameter optimization of the proposed actuator. Full article

This study evaluated the effects of forage-free diets with reduced starch levels on the productive performance, metabolism, ruminal fermentation, nutrient digestibility, and meat quality of feedlot beef cattle. Two experiments were conducted. In Experiment 1, forty uncastrated Nellore steers were distributed into 20 pens in a completely randomized design, receiving diets with increasing inclusion levels of ground corn in the total diet: C400 (400 g kg⁻¹), C200 (200 g kg⁻¹), C100 (100 g kg⁻¹), and C50 (50 g kg⁻¹), formulated without forage and based on fibrous co-products. Increasing ground corn inclusion promoted linear improvements in final body weight and average daily gain, while dry matter intake and feed efficiency showed quadratic responses. Meat quality parameters were not affected by dietary treatments. In Experiment 2, eight crossbred steers were assigned to a double 4 × 4 Latin square design and fed the same experimental diets. Higher corn inclusion increased starch and fat intake, whereas dry matter, organic matter, and protein intake showed quadratic responses. Apparent total-tract digestibility of dry matter, organic matter, and starch also followed a quadratic pattern. Ruminal fermentation parameters were affected by dietary treatments, with greater ammoniacal nitrogen concentrations at higher corn levels and quadratic responses for propionate, butyrate, and methane production. Nitrogen metabolism indicated increased urinary nitrogen and uric acid excretion with increasing dietary corn inclusion. These results demonstrate that forage-free diets based on citrus pulp and soybean hulls with different levels of ground corn can be effectively used in finishing beef cattle, improving performance without impairing meat quality while modulating ruminal fermentation and nutrient utilization. Full article

The present paper proposes an adaptive steering weight allocation strategy based on a non-cooperative Stackelberg game and Model Predictive Control (MPC) for dynamic steering authority allocation in human–machine shared control of intelligent vehicles. First, the human–machine steering interaction is modelled as a Stackelberg game, and the steering control problem is formulated as an MPC optimization problem. The optimal control sequences of the driver and the Advanced Driver Assistance System (ADAS) under game equilibrium are then derived through backward induction. Subsequently, driver behaviour is classified as aggressive, moderate, or conservative according to lateral preview error and lateral acceleration, and the driver state is quantified using parametric indicators. Furthermore, by integrating potential field-based driving risk assessment with human–machine conflict intensity, a fuzzy logic-based dynamic weight adjustment mechanism is constructed. Simulation results show that when the steering intentions of the driver and the ADAS are highly consistent, the proposed strategy can effectively reduce driver workload and improve driving safety. In high-risk driving situations, the strategy automatically transfers more steering authority to the ADAS to enhance safety, whereas under low-risk conditions with strong human–machine steering conflict, greater driver authority is preserved to ensure that the vehicle follows the intended path. Hardware-in-the-loop experiments in lane-changing assistance scenarios further verify the effectiveness of the proposed strategy under different driving styles. Quantitative results show that, compared with manual driving, the proposed strategy reduces the maximum lateral overshoot by 98.75%, 85.54%, and 98.58% for aggressive, moderate, and conservative drivers, respectively. In addition, the peak yaw rate and driver control effort are significantly reduced, indicating smoother vehicle dynamic response and lower steering workload. These results demonstrate that the proposed strategy can effectively improve lane-change stability, reduce driver burden, and maintain safe and coordinated human–machine shared control. Full article

Real-time coal–rock identification is essential for intelligent mining, enabling hazard prevention and geological modeling. However, existing methods often suffer from unclear bit–rock interaction mechanisms, signal distortion, sensor remoteness, or delayed data acquisition, limiting their effectiveness in continuous operations. This study proposes a novel approach for real-time coal–rock identification based on multi-source near-bit drilling data. A near-bit data acquisition system was developed and positioned directly behind the drill bit, integrating sensors to capture high-fidelity parameters—including weight on bit (WOB), torque, rotational speed, rate of penetration (ROP), natural gamma ray, and borehole trajectory—thereby eliminating frictional interference from the drill string. A data-driven theoretical model was established to derive a near-bit drillability index (NDI) for rock strength and to correlate gamma ray responses with lithology. Field trials were conducted in a coal mine in northern Shaanxi, involving over 30 boreholes and systematic core validation. The results demonstrate that the method enables continuous, high-resolution identification of coal–rock interfaces and strength variations along the borehole trajectory, with interpreted results aligning well with core logs and achieving approximately 85% accuracy in strength estimation. By ensuring compatibility with conventional drilling rigs and supporting real-time data transmission and 3D geological updating, this study offers a practical and robust technical pathway for achieving geological transparency and real-time steering in underground coal mining. Full article

To ensure reliable lane change behavior in-wheel motor-driven electric vehicles (IWM-EVs) under steer-by-wire (SBW) failure, this paper presents an integrated lateral–longitudinal lane change control strategy based on differential steering. The control framework and relevant models are first established. An upper-layer model predictive control (MPC) controller is then designed to simultaneously achieve lateral path tracking and longitudinal speed regulation, outputting the desired front-wheel steering angle and acceleration. Finally, a model-free adaptive control (MFAC)-based lower-layer lateral controller transforms the desired steering angle into differential driving torques for the front wheels, while a feedforward–feedback lower-layer longitudinal controller (incorporating drive/brake switching and PI control) computes the required driving torque or braking pressure. Co-simulation in Matlab/Simulink R2022b and CarSim R2020 reveals that the MPC controller designed in this study outperforms the LQR-PID controller, reducing the maximum absolute values of lateral error, heading error, front-wheel steering angle, yaw rate and sideslip angle by 42.9%, 50.0%, 7.8%, 2.8% and 10.3%. The proposed hierarchical control strategy outperforms the compared hierarchical controller, reducing the maximum absolute values of the lateral displacement error, heading error and yaw rate by 17.9%, 6.7%, and 33.3%. These results verify that the strategy can improve trajectory tracking accuracy and achieve basic differential steering functionality in specific scenarios. Full article

To address collision risks in teleoperated mobile robotic vehicles, this study proposes a Human–Machine Interaction-based Advanced Smooth Shared Control (ASSC) system aimed at enhancing obstacle avoidance and achieving smooth shared control between human operators and the automation system. The ASSC system integrates a novel approach using predictive vectors to represent the vehicle’s heading position, automatically adjusting the steering position upon obstacle detection to ensure smooth collision avoidance without changing the driver’s perception. Feedback forces applied to the steering wheel are calculated through an artificial potential field algorithm. Twenty participants were invited to operate the vehicle, providing feedback on the ASSC system’s performance relative to conventional obstacle avoidance methods. Performance metrics such as the effects of communication delays, Time to Complete the Task (TTC), ASSC effectiveness, performance of the delay impact on the ASSC system, and the Number of Obstacle Collisions (NOC) are analyzed. The results demonstrate that the ASSC system significantly outperforms traditional obstacle avoidance methods, providing more precise control in teleoperation. Statistical analysis indicates that the ASSC system improves safety, comfort and operational performance by 12.8%. This research highlights the ASSC system as a promising solution for enhancing automation, safety, and human–machine interaction in teleoperated mobile robotic vehicles. Full article

Autonomous guided vehicle (AGV) navigation in high-density warehouses faces significant challenges due to narrow aisles and complex U-shaped traps. In such environments, traditional sampling-based path planning algorithms often converge slowly and produce suboptimal paths. To solve these issues, a novel hierarchical hybrid planning framework named MEG-RRT* (Multi-objective Evolutionary Guided RRT*) is proposed in this study. The proposed MEG-RRT* integrates an optimization engine based on NSGA-II into the sampling process. It guides exploration direction away from local minima by jointly optimizing convergence efficiency and safety-related objectives. Furthermore, a geometry-aware execution layer is introduced to improve motion through narrow passages and to refine the path structure. This layer includes radar-guided steering, adaptive step-size control, and ancestor shortcut operations. Comparative experiments were conducted in simulated scenarios of complex narrow passages and high-density warehouses to verify the superiority of the proposed MEG-RRT*. In complex narrow passages, the proposed algorithm achieves a 100% success rate; it also reduces convergence time by 43.5% compared to standard RRT* and by 44.9% compared to Informed-RRT*. In warehouse environments, it generates smooth, kinematically favorable paths that are 39% shorter than those produced by RRT-Connect. These results demonstrate that MEG-RRT* balances exploration efficiency and solution optimality, making it well suited for automated logistics applications. Full article

As China accelerates toward carbon neutrality, decrypting the causal drivers of renewable energy expansion is paramount for effective policy design. We develop a hybrid analytical framework bridging data-driven K2 structural learning with expert-informed Bayesian Networks to map the intricate interdependencies between policy instruments, resource endowments, and socio-economic variables. This causal mapping reveals a fundamental paradigm shift from resource-bound growth to institutional-steered expansion, particularly in the solar sector where the Renewable Portfolio Standard (RPS) has superseded natural radiation as the primary determinant for capacity scaling. Forward sensitivity and backward diagnostic analyses demonstrate that achieving high-growth milestones requires a synergistic convergence of technological cost reductions and mandatory consumption quotas; conversely, the absence of RPS leads to a 64% degradation in systemic causal connectivity. These findings underscore the necessity of transitioning from price-side stimuli to structural consumption-side mandates to ensure a resilient energy transition. Ultimately, this framework and the identified causal pathways provide a strategic blueprint for other emerging economies navigating the complex transition from subsidy-dependent to market-resilient renewable energy landscapes under stringent climate constraints. Full article

To address the inherent nonlinearity and time-varying dynamics of tractor steer-by-wire (SbW) hydraulic systems, as well as the inadequacies of empirical PID tuning in achieving rapid dynamic response and high tracking accuracy during headland maneuvers, continuous steering, and stochastic field operations, this study proposes an Improved Sparrow Search Algorithm (ISSA)-PID control strategy. Initially, an SbW hydraulic test bench was established, and an asymmetric dynamic transfer function model of the steering system was identified utilizing the Nelder–Mead simplex method. To overcome the susceptibility of the conventional Sparrow Search Algorithm (SSA) to local optima entrapment and its insufficient population diversity, the Circle chaotic map was employed to enhance the initial population distribution. Furthermore, an adaptive t-distribution mutation strategy was incorporated to coordinate global exploration and local exploitation, facilitating the optimization of the PID parameters. Hardware-in-the-loop (HIL) bench tests were conducted to evaluate the performance of the different control algorithms. With the proposed ISSA-PID controller, under step response conditions, accounting for the inherent dynamics of the asymmetric steering cylinder, the response times for left and right turns were reduced to 0.77 s and 0.98 s, respectively. During random signal tracking tests that emulate stochastic field operations, the average tracking error was minimized to 0.75°, with a maximum deviation restricted to 1.27°. These results demonstrate that the proposed ISSA-PID strategy addresses parameter tuning challenges, improving control precision and dynamic response. Consequently, it offers a practical control strategy for tractor SbW hydraulic systems. Full article

The actuator reliability of Steer-by-Wire (SBW) systems is critical to the functional safety of autonomous vehicles. However, existing control methods struggle to simultaneously enhance both response speed and fault-tolerant performance when facing multiple uncertainties such as parameter perturbations, external disturbances, and actuator faults. To address these issues, this paper proposes a synergistic fault-tolerant control (FTC) strategy combining guaranteed cost control (GCC) and integral sliding mode control (ISMC). First, a dynamic model of the SBW system incorporating the multiple uncertainties is established. Second, a GCC law is derived based on linear matrix inequalities (LMIs) to impose strict constraints on the system’s tracking accuracy and robustness. Building upon this, an ISMC is integrated to significantly accelerate the system’s dynamic response without sacrificing steady-state accuracy, thereby forming a synergistic fault-tolerant architecture characterized by both high precision and rapid response. The results indicate that, under typical fault modes and steering conditions, the response speed of GCC+ISMC is significantly improved compared with GCC alone, and the GCC+ISMC reduces tracking errors by approximately 35% compared to adaptive integral sliding mode control (AISMC). These findings demonstrate that the proposed approach effectively mitigates multiple system uncertainties, offering comprehensive advantages in tracking accuracy, response speed, and robustness. Full article

An electrically tunable wide-beam-scanning metagratings leaky-wave antenna (MGs LWA) based on liquid crystal (LC) is proposed. Two-dimensional (2D) periodic slotted MGs with capacitive and inductive behaviors are etched on the bottom layer of the substrate and backed by a ground plane with an LWA framework. Two different slotted MG elements are adopted to suppress the open-stopband effects. A theoretical analysis is conducted to provide a conceptual framework for the equivalent electromagnetic fields generated by slotted MGs. Using LC, tunable beam scanning is achieved at a fixed frequency. The LC is placed between the inverted MGs LWA radiating metal and the ground plane to control the LC molecules’ orientation angle by applying a DC voltage across them, thereby adjusting the LC permittivity. Using the results obtained, the proposed antenna can be tuned up to 40° at a fixed frequency by applying a biased DC voltage ranging from 0 V to 10 V. The actual operating bandwidth is 40% for continuous beam scanning of 71°, with a scanned sensitivity of 8.35°/GHz at the zero voltage (V = 0 V), and beam scanning of 61°, with a scanned sensitivity of 7.17°/GHz at the saturation voltage (V = 10 V). The proposed MGs LWA has a realized gain of up to 13.84 dBi. Finally, the proposed antenna has excellent performance due to its potential to achieve wide tunable beam scanning with a narrow beamwidth compared to traditional LWAs’ limitation of radiation angle, depending on the excitation frequency, which makes the proposed antenna suitable in terms of range and sensing calibration for operation at a specific frequency in sensing communication and radar applications. Full article